Merge pull request #9091 from ethereum/phaserConversionWarnings

Fixing Phaser signedness conversion warnings
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chriseth 2020-06-05 10:08:49 +02:00 committed by GitHub
commit d4552678a9
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8 changed files with 51 additions and 35 deletions

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@ -118,7 +118,7 @@ BOOST_AUTO_TEST_CASE(geneAddition_should_be_able_to_insert_before_first_position
BOOST_TEST(mutatedChromosome.length() > chromosome.length());
vector<string> suffix(
mutatedChromosome.optimisationSteps().end() - chromosome.length(),
mutatedChromosome.optimisationSteps().end() - static_cast<ptrdiff_t>(chromosome.length()),
mutatedChromosome.optimisationSteps().end()
);
BOOST_TEST(suffix == chromosome.optimisationSteps());
@ -135,7 +135,7 @@ BOOST_AUTO_TEST_CASE(geneAddition_should_be_able_to_insert_after_last_position)
vector<string> prefix(
mutatedChromosome.optimisationSteps().begin(),
mutatedChromosome.optimisationSteps().begin() + chromosome.length()
mutatedChromosome.optimisationSteps().begin() + static_cast<ptrdiff_t>(chromosome.length())
);
BOOST_TEST(prefix == chromosome.optimisationSteps());
}
@ -179,8 +179,8 @@ BOOST_AUTO_TEST_CASE(alternativeMutations_should_choose_between_mutations_with_g
for (size_t i = 0; i < 10; ++i)
{
Chromosome mutatedChromosome = mutation(chromosome);
cCount += static_cast<int>(mutatedChromosome == Chromosome("c"));
fCount += static_cast<int>(mutatedChromosome == Chromosome("f"));
cCount += (mutatedChromosome == Chromosome("c") ? 1 : 0);
fCount += (mutatedChromosome == Chromosome("f") ? 1 : 0);
}
// This particular seed results in 7 "c"s out of 10 which looks plausible given the 80% chance.

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@ -135,7 +135,7 @@ BOOST_FIXTURE_TEST_CASE(makeRandom_should_get_chromosome_lengths_from_specified_
size_t maxLength = 5;
assert(chromosomeCount % maxLength == 0);
auto nextLength = [counter = 0, maxLength]() mutable { return counter++ % maxLength; };
auto nextLength = [counter = 0ul, maxLength]() mutable { return counter++ % maxLength; };
auto population = Population::makeRandom(m_fitnessMetric, chromosomeCount, nextLength);
// We can't rely on the order since the population sorts its chromosomes immediately but

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@ -72,9 +72,13 @@ size_t phaser::test::countDifferences(Chromosome const& _chromosome1, Chromosome
{
size_t count = 0;
for (size_t i = 0; i < min(_chromosome1.length(), _chromosome2.length()); ++i)
count += static_cast<int>(_chromosome1.optimisationSteps()[i] != _chromosome2.optimisationSteps()[i]);
if (_chromosome1.optimisationSteps()[i] != _chromosome2.optimisationSteps()[i])
++count;
return count + abs(static_cast<int>(_chromosome1.length() - _chromosome2.length()));
return count + static_cast<size_t>(abs(
static_cast<long>(_chromosome1.length()) -
static_cast<long>(_chromosome2.length())
));
}
TemporaryDirectory::TemporaryDirectory(std::string const& _prefix):

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@ -155,7 +155,7 @@ Population ClassicGeneticAlgorithm::select(Population _population, size_t _selec
vector<Individual> selectedIndividuals;
for (size_t i = 0; i < _selectionSize; ++i)
{
uint32_t ball = SimulationRNG::uniformInt(0, rouletteRange - 1);
size_t ball = SimulationRNG::uniformInt(0, rouletteRange - 1);
size_t cumulativeFitness = 0;
for (auto const& individual: _population.individuals())

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@ -126,12 +126,12 @@ ChromosomePair fixedPointSwap(
return {
Chromosome(
vector<string>(begin1, begin1 + _crossoverPoint) +
vector<string>(begin2 + _crossoverPoint, end2)
vector<string>(begin1, begin1 + static_cast<ptrdiff_t>(_crossoverPoint)) +
vector<string>(begin2 + static_cast<ptrdiff_t>(_crossoverPoint), end2)
),
Chromosome(
vector<string>(begin2, begin2 + _crossoverPoint) +
vector<string>(begin1 + _crossoverPoint, end1)
vector<string>(begin2, begin2 + static_cast<ptrdiff_t>(_crossoverPoint)) +
vector<string>(begin1 + static_cast<ptrdiff_t>(_crossoverPoint), end1)
),
};
}
@ -196,8 +196,8 @@ ChromosomePair fixedTwoPointSwap(
assert(_crossoverPoint2 <= _chromosome1.length());
assert(_crossoverPoint2 <= _chromosome2.length());
size_t lowPoint = min(_crossoverPoint1, _crossoverPoint2);
size_t highPoint = max(_crossoverPoint1, _crossoverPoint2);
auto lowPoint = static_cast<ptrdiff_t>(min(_crossoverPoint1, _crossoverPoint2));
auto highPoint = static_cast<ptrdiff_t>(max(_crossoverPoint1, _crossoverPoint2));
auto begin1 = _chromosome1.optimisationSteps().begin();
auto begin2 = _chromosome2.optimisationSteps().begin();
@ -282,17 +282,17 @@ ChromosomePair uniformSwap(Chromosome const& _chromosome1, Chromosome const& _ch
if (_chromosome1.length() > minLength)
{
if (swapTail)
steps2.insert(steps2.end(), begin1 + minLength, end1);
steps2.insert(steps2.end(), begin1 + static_cast<ptrdiff_t>(minLength), end1);
else
steps1.insert(steps1.end(), begin1 + minLength, end1);
steps1.insert(steps1.end(), begin1 + static_cast<ptrdiff_t>(minLength), end1);
}
if (_chromosome2.length() > minLength)
{
if (swapTail)
steps1.insert(steps1.end(), begin2 + minLength, end2);
steps1.insert(steps1.end(), begin2 + static_cast<ptrdiff_t>(minLength), end2);
else
steps2.insert(steps2.end(), begin2 + minLength, end2);
steps2.insert(steps2.end(), begin2 + static_cast<ptrdiff_t>(minLength), end2);
}
return {Chromosome(steps1), Chromosome(steps2)};

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@ -30,7 +30,7 @@ vector<tuple<size_t, size_t>> RandomPairSelection::materialise(size_t _poolSize)
if (_poolSize < 2)
return {};
size_t count = static_cast<size_t>(round(_poolSize * m_selectionSize));
auto count = static_cast<size_t>(round(_poolSize * m_selectionSize));
vector<tuple<size_t, size_t>> selection;
for (size_t i = 0; i < count; ++i)
@ -64,7 +64,10 @@ vector<tuple<size_t, size_t>> PairsFromRandomSubset::materialise(size_t _poolSiz
} while (selectedIndices.size() % 2 != 0);
}
else
selectedIndices.erase(selectedIndices.begin() + SimulationRNG::uniformInt(0, selectedIndices.size() - 1));
selectedIndices.erase(
selectedIndices.begin() +
static_cast<ptrdiff_t>(SimulationRNG::uniformInt(0, selectedIndices.size() - 1))
);
}
assert(selectedIndices.size() % 2 == 0);
@ -73,14 +76,14 @@ vector<tuple<size_t, size_t>> PairsFromRandomSubset::materialise(size_t _poolSiz
{
size_t position1 = SimulationRNG::uniformInt(0, selectedIndices.size() - 1);
size_t value1 = selectedIndices[position1];
selectedIndices.erase(selectedIndices.begin() + position1);
selectedIndices.erase(selectedIndices.begin() + static_cast<ptrdiff_t>(position1));
size_t position2 = SimulationRNG::uniformInt(0, selectedIndices.size() - 1);
size_t value2 = selectedIndices[position2];
selectedIndices.erase(selectedIndices.begin() + position2);
selectedIndices.erase(selectedIndices.begin() + static_cast<ptrdiff_t>(position2));
selectedPairs.push_back({value1, value2});
selectedPairs.emplace_back(value1, value2);
}
assert(selectedIndices.size() == 0);
assert(selectedIndices.empty());
return selectedPairs;
}

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@ -17,12 +17,17 @@
#include <tools/yulPhaser/SimulationRNG.h>
// NOTE: The code would work with std::random but the results for a given seed would not be reproducible
// across different STL implementations. Boost does not guarantee this either but at least it has only one
// implementation. Reproducibility is not a hard requirement for yul-phaser but it's nice to have.
#include <boost/random/bernoulli_distribution.hpp>
#include <boost/random/binomial_distribution.hpp>
#include <boost/random/uniform_int_distribution.hpp>
#include <ctime>
#include <limits>
using namespace std;
using namespace solidity;
using namespace solidity::phaser;
@ -30,23 +35,27 @@ thread_local boost::random::mt19937 SimulationRNG::s_generator(SimulationRNG::ge
bool SimulationRNG::bernoulliTrial(double _successProbability)
{
boost::random::bernoulli_distribution<> distribution(_successProbability);
boost::random::bernoulli_distribution<double> distribution(_successProbability);
return static_cast<bool>(distribution(s_generator));
}
uint32_t SimulationRNG::uniformInt(uint32_t _min, uint32_t _max)
{
boost::random::uniform_int_distribution<> distribution(_min, _max);
return distribution(s_generator);
}
uint32_t SimulationRNG::binomialInt(uint32_t _numTrials, double _successProbability)
size_t SimulationRNG::uniformInt(size_t _min, size_t _max)
{
boost::random::binomial_distribution<> distribution(_numTrials, _successProbability);
boost::random::uniform_int_distribution<size_t> distribution(_min, _max);
return distribution(s_generator);
}
size_t SimulationRNG::binomialInt(size_t _numTrials, double _successProbability)
{
// NOTE: binomial_distribution<size_t> would not work because it internally tries to use abs()
// and fails to compile due to ambiguous conversion.
assert(_numTrials <= static_cast<size_t>(numeric_limits<long>::max()));
boost::random::binomial_distribution<long> distribution(static_cast<long>(_numTrials), _successProbability);
return static_cast<size_t>(distribution(s_generator));
}
uint32_t SimulationRNG::generateSeed()
{
// This is not a secure way to seed the generator but it's good enough for simulation purposes.

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@ -38,8 +38,8 @@ class SimulationRNG
{
public:
static bool bernoulliTrial(double _successProbability);
static uint32_t uniformInt(uint32_t _min, uint32_t _max);
static uint32_t binomialInt(uint32_t _numTrials, double _successProbability);
static size_t uniformInt(size_t _min, size_t _max);
static size_t binomialInt(size_t _numTrials, double _successProbability);
/// Resets generator to a known state given by the @a seed. Given the same seed, a fixed
/// sequence of calls to the members generating random values is guaranteed to produce the